L2 - Cloudera Public Cloud Platform Engineer

L2 – Cloudera Public Cloud Platform Engineer

Client: Gainwell

Work location: Remote work accepted from anywhere in US

Rate: bill rate amt/hour (Some flexibility for strong candidates)

4–6 years of experience in Big Data Platform / Cloud Operations / Infrastructure Support roles

3+ years of experience with Cloudera ecosystem (CDH/CDP)

Hands-on experience with:

• CDP services (CDE, CDW, CDF, CAI)
• Monitoring, alerting, and operational support

Strong understanding of:

• Cloud platforms (AWS/Azure/GCP basics – IAM, storage, networking)
• Kubernetes concepts (pod-level troubleshooting, logs, resource usage)

Experience in:

• Production support and incident handling (P2/P3)
• Platform monitoring, issue triage, and escalation

Responsible for:

• Executing day-to-day platform operations
• Supporting deployments, monitoring, and troubleshooting
• Following runbooks and operational procedures

Required Skills:

• Strong experience with Cloudera CDP Public Cloud
• Expertise in:
• Cloud platforms (AWS/Azure/GCP)
• Kubernetes concepts (troubleshooting-focused)
• Hands-on with:
• CDE, CDW, CDF (NiFi), CAI
• Knowledge of IAM, networking, observability tools
• Platforms operating at multi-terabyte to petabyte scale with high concurrency workloads
• Hands-on experience with:
• Kafka (or similar streaming platforms) including monitoring, troubleshooting, and performance tuning
• Cloudera CDP CLI (Command Line Interface) for:
• Platform operations and administration
• Job execution and service management (CDE/CDW/CDL)
• Automation of routine operational tasks
• Strong working knowledge of:
• Cloud IAM (AWS IAM / Azure AD) including roles, policies, and cross-service access
• User and group mapping across CDP, cloud IAM, and Ranger policies
• Troubleshooting access issues across storage (S3/ADLS), CDP services, and data access layers

Preferred Skills:

• Experience with:
• Modernization of legacy data platforms/applications to Cloudera CDP Public Cloud
• Migration and onboarding of workloads to CDE, CDW, and CAI environments
• Supporting hybrid or multi-environment transitions (on-prem → cloud)
• Familiarity with:
• Cloud platforms (AWS, Azure, GCP) including storage, IAM, and networking concepts
• Kubernetes-based runtime environments (troubleshooting-focused)
• Strong scripting and automation skills (Python, Shell, Terraform) for platform operations

What You’ll Work On:

• Enterprise-scale Cloudera CDP platform supporting data engineering, analytics, and AI workloads across multiple applications
• Modernization of legacy platforms and applications into cloud-native CDP services
• Operational support and scaling of:
• Data services (CDE, CDW, CDF, CDL)
• AI/ML platforms (CAI, inference, workbenches)
• Platform performance optimization, observability, and reliability engineering for mission-critical workloads

Why This Role Matters:

• Ensures availability, stability, and performance of the CDP platform supporting all data and AI workloads
• Enables successful modernization of legacy applications into scalable, cloud-native services
• Maintains high availability, observability, and operational excellence across enterprise platforms
• Acts as the backbone for data engineering, analytics, and AI initiatives
• This role focuses on platform reliability and infrastructure operations and does not include data-layer ownership (e.g., Iceberg table management or data validation).

Job Summary:

We are seeking a highly skilled Cloudera Public Cloud Platform Engineer to operate and manage the end-to-end CDP platform ecosystem, including data services, NiFI, Kafka, AI/ML platforms, and enterprise observability. This role is responsible for ensuring availability, scalability, security, and performance of all platform services supporting data, analytics, and AI workloads across environments. The ideal candidate brings strong expertise in CDP on-prem, public cloud services, cloud infrastructure, Kubernetes-based runtime environments, and platform observability, supporting high-concurrency, mission-critical workloads at multi-terabyte to petabyte scale. This role is critical to ensuring uninterrupted operation of data, analytics, and AI platforms—any degradation directly impacts downstream business reporting, data pipelines, and model execution.